{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e2a6176",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 20 30] 40\n",
      "[20 30 40] 50\n",
      "[30 40 50] 60\n",
      "[40 50 60] 70\n",
      "[50 60 70] 80\n",
      "[60 70 80] 90\n"
     ]
    }
   ],
   "source": [
    "# univariate data preparation\n",
    "from numpy import array\n",
    "import pandas as pd\n",
    "# split a univariate sequence into samples\n",
    "def split_sequence(sequence, n_steps):\n",
    "    X, y = list(), list()\n",
    "    for i in range(len(sequence)):\n",
    "        # find the end of this pattern\n",
    "        end_ix = i + n_steps\n",
    "        # check if we are beyond the sequence\n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "        # gather input and output parts of the pattern\n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    " \n",
    "# define input sequence\n",
    "raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "# choose a number of time steps\n",
    "n_steps = 4\n",
    "# split into samples\n",
    "X, y = split_sequence(range_data[['PM2.5','TEMP', 'RH', 'PA', 'WIND']].values, n_steps)\n",
    "# summarize the data\n",
    "for i in range(len(X)):\n",
    "    print(X[i], y[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba0e4714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>RH</th>\n",
       "      <th>PA</th>\n",
       "      <th>WIND</th>\n",
       "      <th>aqi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>296.60</td>\n",
       "      <td>295.60</td>\n",
       "      <td>23.13</td>\n",
       "      <td>100.73</td>\n",
       "      <td>3.44</td>\n",
       "      <td>6.50</td>\n",
       "      <td>273.76</td>\n",
       "      <td>39.15</td>\n",
       "      <td>100369.12</td>\n",
       "      <td>1.352036</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>472.55</td>\n",
       "      <td>465.82</td>\n",
       "      <td>27.29</td>\n",
       "      <td>116.78</td>\n",
       "      <td>5.61</td>\n",
       "      <td>5.39</td>\n",
       "      <td>273.35</td>\n",
       "      <td>41.42</td>\n",
       "      <td>101946.08</td>\n",
       "      <td>0.358469</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>492.42</td>\n",
       "      <td>482.45</td>\n",
       "      <td>31.50</td>\n",
       "      <td>119.89</td>\n",
       "      <td>6.11</td>\n",
       "      <td>5.19</td>\n",
       "      <td>272.77</td>\n",
       "      <td>43.60</td>\n",
       "      <td>102247.25</td>\n",
       "      <td>2.434831</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>464.30</td>\n",
       "      <td>460.67</td>\n",
       "      <td>31.82</td>\n",
       "      <td>114.53</td>\n",
       "      <td>5.91</td>\n",
       "      <td>4.79</td>\n",
       "      <td>272.29</td>\n",
       "      <td>45.32</td>\n",
       "      <td>102274.90</td>\n",
       "      <td>3.586879</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>430.77</td>\n",
       "      <td>433.55</td>\n",
       "      <td>31.06</td>\n",
       "      <td>111.65</td>\n",
       "      <td>5.66</td>\n",
       "      <td>4.68</td>\n",
       "      <td>271.90</td>\n",
       "      <td>47.59</td>\n",
       "      <td>102408.89</td>\n",
       "      <td>3.570658</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31 23:00:00</th>\n",
       "      <td>8.06</td>\n",
       "      <td>12.01</td>\n",
       "      <td>2.57</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0.25</td>\n",
       "      <td>58.22</td>\n",
       "      <td>265.15</td>\n",
       "      <td>39.45</td>\n",
       "      <td>97035.02</td>\n",
       "      <td>7.175974</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31 23:00:00</th>\n",
       "      <td>9.16</td>\n",
       "      <td>14.62</td>\n",
       "      <td>2.73</td>\n",
       "      <td>6.52</td>\n",
       "      <td>0.27</td>\n",
       "      <td>58.20</td>\n",
       "      <td>265.24</td>\n",
       "      <td>40.49</td>\n",
       "      <td>97197.85</td>\n",
       "      <td>7.411511</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31 23:00:00</th>\n",
       "      <td>7.31</td>\n",
       "      <td>10.59</td>\n",
       "      <td>2.51</td>\n",
       "      <td>5.64</td>\n",
       "      <td>0.25</td>\n",
       "      <td>58.48</td>\n",
       "      <td>263.93</td>\n",
       "      <td>41.01</td>\n",
       "      <td>95287.30</td>\n",
       "      <td>6.485985</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31 23:00:00</th>\n",
       "      <td>7.37</td>\n",
       "      <td>10.97</td>\n",
       "      <td>2.44</td>\n",
       "      <td>5.17</td>\n",
       "      <td>0.25</td>\n",
       "      <td>58.35</td>\n",
       "      <td>263.98</td>\n",
       "      <td>42.61</td>\n",
       "      <td>95685.62</td>\n",
       "      <td>6.956048</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31 23:00:00</th>\n",
       "      <td>7.65</td>\n",
       "      <td>11.77</td>\n",
       "      <td>2.46</td>\n",
       "      <td>5.37</td>\n",
       "      <td>0.25</td>\n",
       "      <td>57.79</td>\n",
       "      <td>263.54</td>\n",
       "      <td>43.27</td>\n",
       "      <td>95104.91</td>\n",
       "      <td>7.368372</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112344 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      PM2.5    PM10    SO2     NO2    CO     O3    TEMP  \\\n",
       "time                                                                      \n",
       "2017-01-01 00:00:00  296.60  295.60  23.13  100.73  3.44   6.50  273.76   \n",
       "2017-01-01 00:00:00  472.55  465.82  27.29  116.78  5.61   5.39  273.35   \n",
       "2017-01-01 00:00:00  492.42  482.45  31.50  119.89  6.11   5.19  272.77   \n",
       "2017-01-01 00:00:00  464.30  460.67  31.82  114.53  5.91   4.79  272.29   \n",
       "2017-01-01 00:00:00  430.77  433.55  31.06  111.65  5.66   4.68  271.90   \n",
       "...                     ...     ...    ...     ...   ...    ...     ...   \n",
       "2017-01-31 23:00:00    8.06   12.01   2.57    5.52  0.25  58.22  265.15   \n",
       "2017-01-31 23:00:00    9.16   14.62   2.73    6.52  0.27  58.20  265.24   \n",
       "2017-01-31 23:00:00    7.31   10.59   2.51    5.64  0.25  58.48  263.93   \n",
       "2017-01-31 23:00:00    7.37   10.97   2.44    5.17  0.25  58.35  263.98   \n",
       "2017-01-31 23:00:00    7.65   11.77   2.46    5.37  0.25  57.79  263.54   \n",
       "\n",
       "                        RH         PA      WIND  aqi  \n",
       "time                                                  \n",
       "2017-01-01 00:00:00  39.15  100369.12  1.352036   50  \n",
       "2017-01-01 00:00:00  41.42  101946.08  0.358469   58  \n",
       "2017-01-01 00:00:00  43.60  102247.25  2.434831   61  \n",
       "2017-01-01 00:00:00  45.32  102274.90  3.586879   59  \n",
       "2017-01-01 00:00:00  47.59  102408.89  3.570658   57  \n",
       "...                    ...        ...       ...  ...  \n",
       "2017-01-31 23:00:00  39.45   97035.02  7.175974   18  \n",
       "2017-01-31 23:00:00  40.49   97197.85  7.411511   18  \n",
       "2017-01-31 23:00:00  41.01   95287.30  6.485985   18  \n",
       "2017-01-31 23:00:00  42.61   95685.62  6.956048   18  \n",
       "2017-01-31 23:00:00  43.27   95104.91  7.368372   18  \n",
       "\n",
       "[112344 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataAll = pd.read_csv('./data.csv')\n",
    "DataAll.index = DataAll['time']\n",
    "DataAll.drop('time', axis =1,  inplace = True)\n",
    "DataAll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "664acce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.96600000e+02 2.95600000e+02 2.31300000e+01 1.00730000e+02\n",
      "  3.44000000e+00 6.50000000e+00 2.73760000e+02 3.91500000e+01\n",
      "  1.00369120e+05 1.35203550e+00 5.00000000e+01]\n",
      " [1.96580000e+02 2.37140000e+02 1.83100000e+01 9.75500000e+01\n",
      "  3.08000000e+00 9.27000000e+00 2.74330000e+02 3.71800000e+01\n",
      "  1.00309090e+05 2.73607383e+00 4.90000000e+01]\n",
      " [1.89070000e+02 2.25000000e+02 1.68800000e+01 9.83900000e+01\n",
      "  3.20000000e+00 9.55000000e+00 2.75000000e+02 3.53500000e+01\n",
      "  1.00273290e+05 2.54001968e+00 4.90000000e+01]] [1.70350000e+02 1.99270000e+02 1.45400000e+01 9.88700000e+01\n",
      " 2.94000000e+00 1.09900000e+01 2.75250000e+02 3.48000000e+01\n",
      " 1.00249140e+05 2.35849528e+00 4.90000000e+01]\n"
     ]
    }
   ],
   "source": [
    "X, y = split_sequence(range_data.values, n_steps)\n",
    "# summarize the data\n",
    "for i in range(len(X)):\n",
    "    print(X[i], y[i])\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad0c92e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "range_data = DataAll.groupby(DataAll.index).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c359a7de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# univariate lstm example\n",
    "from numpy import array\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense\n",
    " \n",
    "# split a univariate sequence into samples\n",
    "def split_sequence(sequence, n_steps):\n",
    "\tX, y = list(), list()\n",
    "\tfor i in range(len(sequence)):\n",
    "\t\t# find the end of this pattern\n",
    "\t\tend_ix = i + n_steps\n",
    "\t\t# check if we are beyond the sequence\n",
    "\t\tif end_ix > len(sequence)-1:\n",
    "\t\t\tbreak\n",
    "\t\t# gather input and output parts of the pattern\n",
    "\t\tseq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "\t\tX.append(seq_x)\n",
    "\t\ty.append(seq_y)\n",
    "\treturn array(X), array(y)\n",
    " \n",
    "# define input sequence\n",
    "raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "# choose a number of time steps\n",
    "n_steps = 3\n",
    "# split into samples\n",
    "X, y = split_sequence(raw_seq, n_steps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb01a083",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcad4fb4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8646eff2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c697c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e26d480",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5991a37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8ede11a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6c224108",
   "metadata": {},
   "outputs": [],
   "source": [
    "range_y = range_data['aqi'][3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72b590d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "208b7116",
   "metadata": {},
   "outputs": [],
   "source": [
    "range_x, y = split_sequence(range_data[['PM2.5','TEMP', 'RH', 'PA', 'WIND']].values, n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3e34e44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(741, 3, 5)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reshape from [samples, timesteps] into [samples, timesteps, features]\n",
    "n_features = 5\n",
    "X = X.reshape((X.shape[0], X.shape[1], n_features))\n",
    "# define model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "# fit model\n",
    "model.fit(X, y, epochs=200, verbose=0)\n",
    "# demonstrate prediction\n",
    "x_input = array([70, 80, 90])\n",
    "x_input = x_input.reshape((1, n_steps, n_features))\n",
    "yhat = model.predict(x_input, verbose=0)\n",
    "print(yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3ddcfc46",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_features = 5\n",
    "range_x = range_x.reshape((range_x.shape[0],range_x.shape[1],n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9b815878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2120921fca0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "# fit model\n",
    "model.fit(range_x, range_y, epochs=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0421e148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[38.095432]\n",
      " [35.40842 ]\n",
      " [33.47922 ]\n",
      " [32.538303]\n",
      " [32.43723 ]\n",
      " [33.214573]\n",
      " [34.273167]\n",
      " [32.625706]\n",
      " [30.557837]\n",
      " [29.99729 ]\n",
      " [29.359106]\n",
      " [30.705786]\n",
      " [32.808323]\n",
      " [35.04807 ]\n",
      " [37.282932]\n",
      " [39.750706]\n",
      " [40.7717  ]\n",
      " [39.682346]\n",
      " [36.7092  ]\n",
      " [34.086155]\n",
      " [32.915257]\n",
      " [32.031956]\n",
      " [30.984106]\n",
      " [30.188208]\n",
      " [30.65354 ]\n",
      " [29.902563]\n",
      " [28.237036]\n",
      " [26.263403]\n",
      " [24.978247]\n",
      " [23.807837]\n",
      " [22.612524]\n",
      " [22.347876]\n",
      " [22.801977]\n",
      " [23.612524]\n",
      " [27.249731]\n",
      " [33.321995]\n",
      " [39.253635]\n",
      " [41.63059 ]\n",
      " [41.898167]\n",
      " [42.15012 ]\n",
      " [40.940647]\n",
      " [37.74094 ]\n",
      " [32.910374]\n",
      " [31.219458]\n",
      " [31.82932 ]\n",
      " [34.19553 ]\n",
      " [34.14719 ]\n",
      " [34.46262 ]\n",
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      " [20.256079]\n",
      " [20.352758]\n",
      " [20.314184]\n",
      " [19.520727]\n",
      " [18.131079]\n",
      " [17.492407]\n",
      " [18.194067]\n",
      " [18.256567]\n",
      " [17.235083]\n",
      " [16.846899]\n",
      " [16.767797]\n",
      " [16.751684]\n",
      " [14.883032]\n",
      " [15.877172]\n",
      " [16.432837]\n",
      " [14.964087]\n",
      " [14.154028]\n",
      " [14.254126]\n",
      " [15.970434]\n",
      " [16.618872]\n",
      " [16.202368]\n",
      " [17.488989]\n",
      " [18.433813]\n",
      " [19.17356 ]\n",
      " [19.595434]\n",
      " [20.22141 ]\n",
      " [20.539282]\n",
      " [20.231176]\n",
      " [19.297583]\n",
      " [19.54856 ]\n",
      " [21.506567]\n",
      " [22.09934 ]\n",
      " [22.629614]\n",
      " [24.459204]\n",
      " [26.220922]\n",
      " [26.459204]\n",
      " [25.507055]\n",
      " [23.859106]\n",
      " [23.601782]\n",
      " [22.226294]\n",
      " [21.960669]\n",
      " [22.129614]\n",
      " [22.742407]\n",
      " [24.11936 ]\n",
      " [24.953833]\n",
      " [25.697973]\n",
      " [26.547583]\n",
      " [27.001196]\n",
      " [26.237036]\n",
      " [25.901587]\n",
      " [25.499731]]\n"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(range_x, verbose=0)\n",
    "print(yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1e6b6115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\tensor\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 39044 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Anaconda\\envs\\tensor\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 27979 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Anaconda\\envs\\tensor\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 21407 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Anaconda\\envs\\tensor\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 26469 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Anaconda\\envs\\tensor\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 39044 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Anaconda\\envs\\tensor\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 27979 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Anaconda\\envs\\tensor\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 21407 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Anaconda\\envs\\tensor\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 26469 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = pd.DataFrame([yhat.reshape(1,-1)[0].tolist(), range_y]).T\n",
    "test.index = range_y.index\n",
    "test.columns=['预测','原来']\n",
    "test.plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b3ff629",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aa6da30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa3f6fa3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d0bb152",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25ee6b4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15de5b0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d788d1d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12e730a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "759993b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ab39c59",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8678c34",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36376650",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensor",
   "language": "python",
   "name": "tensor"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
